@article{z2,
    author = {徐启华 and 师军},
    title = {基于支持向量机的航空发动机故障诊断},
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    volume = {20},
    number = {2},
    pages = {298--302},
    month = {apr},
    publisher = {中国航空学会},
    address = {北京, 中国},
    url = {https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFD2005&filename=HKDI200502023},
    language = {chinese},
    issn = {1000-8055}
}

@inproceedings{chakrabarty2018statistical,
    author={Chakrabarty, Navoneel and Biswas, Sanket},
    title={A statistical approach to adult census income level prediction},
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    doi={10.1109/ICACCCN.2018.8748660},
    url={https://ieeexplore.ieee.org/document/8748660}
}

@inproceedings{subasi2019prediction,
    author={Subasi, Abdulhamit and Cankurt, Selcuk},
    title={Prediction of default payment of credit card clients using Data Mining Techniques},
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    year={2019},
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    pages={115--120},
    publisher={IEEE},
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    isbn={978-1-7281-3850-4},
    doi={10.1109/IEC47844.2019.8950616},
    url={https://ieeexplore.ieee.org/document/8950616},
    organization={IEEE}
}

@article{fitriani2021data,
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    journal={JUITA: Jurnal Informatika},
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    url={https://ejurnal.budiluhur.ac.id/index.php/juita/article/view/1429},
    publisher={Universitas Budi Luhur},
    address={Jakarta, Indonesia}
}

@manual{学位论文编写规则,
    author = {{国务院学位委员会办公室} and {中国科学技术信息研究所}},
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    year = {2006},
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    urldate = {2021-08-08},
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@book{汪继祥2004科学出版社作者编辑手册,
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    language = {chinese}
}

@book{全国科学道德和学风建设宣讲教育领导小组2012科学道德与学风建设宣讲参考大纲,
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    publisher = {中国科学技术协会},
    address = {北京},
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    month = {11},
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    language = {zh}
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@article{曹敏2005新版《文后参考文献著录规则》解析,
    author = {曹敏},
    title = {文后参考文献著录规则},
    journal = {科技与出版},
    year = {2005},
    volume = {000},
    number = {006},
    pages = {61--63},
    month = {June},
    publisher = {中国出版协会科技出版工作委员会},
    address = {北京},
    issn = {1005-0590},
    language = {zh},
    keywords = {参考文献著录规则; 国家标准; 编辑规范},
    abstract = {本文详细解读了新版《文后参考文献著录规则》(GB/T 7714-2005)的主要修改内容及其在编辑出版实践中的应用要点。},
    note = {国家标准化管理委员会2005年发布的新版参考文献著录标准解读}
}

@phdthesis{王兰芬2010Swarm,
    author={王兰芬},
    title={Swarm突现计算模型的稳定性研究},
    school={重庆邮电大学},
    year={2010},
    address={Chongqing, China},
    month={6},
    type={博士论文},
    url={http://lib.cqupt.edu.cn/},
    note={Available from Chongqing University of Posts and Telecommunications Library}
}

@book{谢希仁2006计算机网络教程,
    author = {谢希仁},
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    publisher = {电子工业出版社},
    year = {2006},
    address = {Beijing, China},
    edition = {2nd},
    isbn = {978-7-121-02308-3},
    month = {January},
    note = {普通高等教育"十一五"国家级规划教材}
}

@book{2000Experiments,
    author={Buchla, D. and Floyd, T. L.},
    title={Experiments in Digital Fundamentals to Accompany Floyd, Digital Fundamentals, Seventh Edition},
    year={2000},
    publisher={Prentice Hall},
    address={Upper Saddle River, NJ},
    edition={7th},
    isbn={0130892986}
}

@book{2003数字电路简明教程,
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    publisher = {高等教育出版社},
    year = {2003},
    address = {北京},
    isbn = {7-04-012533-1},
    month = {8},
    note = {普通高等教育“十五”国家级规划教材},
    series = {电子信息学科基础课程系列教材},
    edition = {第1版}
}

@article{吕学勤2013求解,
    author = {吕学勤 and 陈树果 and 林静},
    title = {求解0/1背包问题的自适应遗传退火算法},
    journal = {重庆邮电大学学报(自然科学版)},
    year = {2013},
    volume = {25},
    number = {1},
    pages = {138-142},
    month = {feb},
    publisher = {Chongqing University of Posts and Telecommunications},
    address = {Chongqing, China},
    issn = {1673-825X},
    language = {Chinese},
    abstract = {针对0/1背包问题的特点,提出了一种自适应遗传退火算法。该算法结合遗传算法和模拟退火算法的优点,采用自适应的交叉概率和变异概率,有效避免了早熟收敛问题。通过典型实例仿真测试,结果表明该算法在求解质量和收敛速度方面均优于传统遗传算法。},
    keywords = {0/1背包问题; 遗传算法; 模拟退火; 自适应},
    url = {https://kns.cnki.net/kcms2/article/abstract?v=3uoqIhG8C44YLTlOAiTRKibYlV5Vjs7i8oRR1PAr7RxjuAJk4dHXoj90RDiGQ9yaliIjR3ONAnW2MCDD3hYh_6gRgftIHE0Z&uniplatform=NZKPT}
}

@article{2010The,
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    journal = {Computer Networks},
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    number = {15},
    pages = {2787--2805},
    month = {oct},
    doi = {10.1016/j.comnet.2010.05.010},
    url = {https://www.sciencedirect.com/science/article/pii/S1389128610001568},
    publisher = {Elsevier {BV}},
    address = {Amsterdam, The Netherlands},
    issn = {1389-1286}
}

@inproceedings{2011Multi,
    author={Mohammed Alkhawlani and K. Alsalem and A. Hussein},
    title={Multi-criteria Vertical Handover by {TOPSIS} and fuzzy logic},
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    year={2011},
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    isbn={978-1-61284-254-7},
    doi={10.1109/ICCITechnol.2011.5768731},
    url={https://ieeexplore.ieee.org/document/5768731}
}

@article{1999Automatic,
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    url={https://link.springer.com/article/10.1023/A:1009781511299}
}

@phdthesis{伏梦盈0基于博弈论的协作通信中继节点选择,
    author={伏梦盈},
    title={基于博弈论的协作通信中继节点选择},
    school={湖南大学},
    year={2020},
    address={Changsha, China},
    month={jun},
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    note={Available from Hunan University Library}
}

@techreport{工信部电信研究院0物联网白皮书,
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    institution = {工业和信息化部电信研究院},
    year = {2011},
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    month = {12},
    type = {白皮书},
    language = {chinese},
    note = {中华人民共和国工业和信息化部官方报告}
}

@article{胡友良2011学术论文格式规范举要,
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    year = {2011},
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    publisher = {中国内部审计协会},
    address = {Beijing, China},
    language = {Chinese},
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    note = {Accessed via China National Knowledge Infrastructure (CNKI)}
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@patent{陈国平0一种基于蓝牙技术的手机防盗防遗失报警方法,
    author={陈国平 and 张百珂 and 马耀辉},
    title={一种基于蓝牙技术的手机防盗防遗失报警方法},
    nationality={CN},
    number={CN103020588A},
    year={2013},
    month={may},
    holder={浙江工业大学},
    url={http://epub.cnipa.gov.cn/CN103020588A},
    address={中国},
    note={中国发明专利，申请号：CN201210332966.3},
    keywords={蓝牙技术; 手机防盗; 报警系统; 移动通信},
    urldate={2023-10-15}
}

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    url = {http://www.cnblogs.com/magicboy110/archive/2010/12/09/1901669.html},
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}

@misc{工业和信息化部关于电信服务质量的通告(2014年第1号）,
    author = {{中华人民共和国工业和信息化部}},
    title = {工业和信息化部关于电信服务质量的通告(2014年第1号)},
    year = {2014},
    month = {March},
    url = {http://www.miit.gov.cn/n11293472/n11293832/n11293907/n11368223/15864477.html},
    urldate = {2014-03-03},
    publisher = {工业和信息化部},
    address = {北京},
    number = {1},
    type = {政府公告},
    language = {Chinese},
    institution = {中华人民共和国工业和信息化部}
}

@article{kang2022fedcvt,
    author={Kang, Yan and Liu, Yang and Liang, Xinle},
    title={FedCVT: Semi-supervised vertical federated learning with cross-view training},
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    year={2022},
    volume={13},
    number={4},
    pages={1--16},
    month={jul},
    publisher={ACM},
    address={New York, NY, USA},
    doi={10.1145/3546789},
    url={https://dl.acm.org/doi/10.1145/3546789},
    issn={2157-6912}
}

@inproceedings{oliver2018realistic,
    author={Oliver, Avital and Odena, Augustus and Raffel, Colin A. and Cubuk, Ekin Dogus and Goodfellow, Ian},
    title={Realistic Evaluation of Deep Semi-Supervised Learning Algorithms},
    booktitle={Advances in Neural Information Processing Systems},
    year={2018},
    volume={31},
    pages={3235--3246},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
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    doi={10.48550/arXiv.1804.09170},
    month={December},
    series={NeurIPS '18}
}

@inproceedings{tarvainen2017mean,
    author={Tarvainen, Antti and Valpola, Harri},
    title={Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results},
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    year={2017},
    volume={30},
    pages={1195--1204},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
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    doi={10.5555/3294996.3295073},
    month={December}
}

@misc{zhang2017mixup,
    author = {Zhang, Hongyi and Cisse, Moustapha and Dauphin, Yann N. and Lopez-Paz, David},
    title = {mixup: Beyond Empirical Risk Minimization},
    year = {2017},
    month = {oct},
    howpublished = {arXiv preprint},
    archiveprefix = {arXiv},
    eprint = {1710.09412},
    primaryclass = {cs.LG},
    url = {https://arxiv.org/abs/1710.09412},
}

@article{chen2023softmatch,
    author={Chen, Hao and Tao, Ran and Fan, Yue and Wang, Yidong and Wang, Jindong and Schiele, Bernt and Xie, Xing and Raj, Bhiksha and Savvides, Marios},
    title={SoftMatch: Addressing the Quantity-Quality Trade-off in Semi-Supervised Learning},
    journal={arXiv preprint arXiv:2301.10921},
    year={2023},
    month={January},
    url={https://arxiv.org/abs/2301.10921},
    eprint={2301.10921},
    archivePrefix={arXiv},
    primaryClass={cs.LG},
    abstract={Semi-supervised learning (SSL) addresses the challenge of labeled data scarcity by leveraging both labeled and unlabeled data. However, existing SSL methods commonly adopt a fixed thresholding approach to select pseudo-labels for training, leading to a trade-off between the quantity and quality of pseudo-labels. This work introduces SoftMatch to overcome this trade-off by maintaining a unified Gaussian distribution for all pseudo-labels. We theoretically derive a truncated Gaussian function to weight the pseudo-labels, effectively balancing the quantity and quality of pseudo-labels during training. Extensive experiments demonstrate that SoftMatch achieves state-of-the-art performance across a variety of benchmarks, including image, text, and imbalanced classification tasks.}
}

@article{berthelot2021adamatch,
    author={Berthelot, David and Roelofs, Rebecca and Sohn, Kihyuk and Carlini, Nicholas and Kurakin, Alex},
    title={AdaMatch: A Unified Approach to Semi-Supervised Learning and Domain Adaptation},
    journal={arXiv preprint arXiv:2106.04732},
    year={2021},
    month={June},
    url={https://arxiv.org/abs/2106.04732},
    doi={10.48550/arXiv.2106.04732},
    publisher={arXiv},
    address={Ithaca, NY},
    eprint={2106.04732},
    archiveprefix={arXiv},
    primaryclass={cs.LG}
}

@inproceedings{li2021comatch,
    author={Junnan Li and Caiming Xiong and Steven C. H. Hoi},
    title={CoMatch: Semi-Supervised Learning with Contrastive Graph Regularization},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    year={2021},
    pages={9475--9484},
    publisher={IEEE Computer Society},
    address={Los Alamitos, CA, USA},
    month={October},
    doi={10.1109/ICCV48922.2021.00936},
    url={https://doi.org/10.1109/ICCV48922.2021.00936},
    isbn={978-1-6654-2812-5}
}

@inproceedings{sohn2020fixmatch,
    author={Sohn, Kihyuk and Berthelot, David and Carlini, Nicholas and Zhang, Zizhao and Zhang, Han and Raffel, Colin A. and Cubuk, Ekin Dogus and Kurakin, Alexey and Li, Chun-Liang},
    title={FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence},
    booktitle={Advances in Neural Information Processing Systems},
    year={2020},
    volume={33},
    pages={596--608},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
    month={December},
    url={https://proceedings.neurips.cc/paper/2020/hash/06964dce9addb1b3fc5321ab7f4a8293-Abstract.html},
    doi={10.48550/arXiv.2001.07685}
}

@article{berthelot2019mixmatch,
    author={Berthelot, David and Carlini, Nicholas and Goodfellow, Ian and Papernot, Nicolas and Oliver, Avital and Raffel, Colin A},
    title={MixMatch: A Holistic Approach to Semi-Supervised Learning},
    journal={Advances in Neural Information Processing Systems},
    volume={32},
    pages={5049--5059},
    year={2019},
    month={December},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
    url={https://proceedings.neurips.cc/paper/2019/hash/1cd138d0499a68f4bb72bee04bbf2a48-Abstract.html},
    doi={10.48550/arXiv.1905.02249},
    note={arXiv preprint arXiv:1905.02249}
}

@inproceedings{lee2013pseudo,
    author={Lee, Dong-Hyun},
    title={Pseudo-label: The simple and efficient semi-supervised learning method for deep neural networks},
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    volume={28},
    pages={896--903},
    year={2013},
    month={June},
    publisher={JMLR.org},
    address={Atlanta, GA, USA},
    url={http://proceedings.mlr.press/v28/lee13.html},
    doi={10.5555/3042817.3042970}
}

@inproceedings{xu2017multi,
    author={Xu, Yixing and Xu, Chang and Xu, Chao and Tao, Dacheng},
    title={Multi-Positive and Unlabeled Learning},
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    year={2017},
    pages={3182--3188},
    publisher={IJCAI Organization},
    address={Melbourne, Australia},
    month={August},
    editor={Sierra, Carles},
    volume={7},
    doi={10.24963/ijcai.2017/444},
    url={http://www.ijcai.org/proceedings/2017/0444.pdf}
}

@inproceedings{de2010practical,
    author={De Cristofaro, Emiliano and Tsudik, Gene},
    title={Practical Private Set Intersection Protocols with Linear Complexity},
    booktitle={Financial Cryptography and Data Security: 14th International Conference, FC 2010, Tenerife, Canary Islands, January 25-28, 2010, Revised Selected Papers 14},
    year={2010},
    editor={Sion, Radu and others},
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    isbn={978-3-642-14576-6},
    issn={1611-3349}
}

@article{mordelet2014bagging,
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    address = {Amsterdam, Netherlands}
}

@article{claesen2015robust,
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    year = {2015},
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    url = {https://www.sciencedirect.com/science/article/pii/S0925231214015753}
}

@inproceedings{zheng2018drn,
    author={Zheng, Guanjie and Zhang, Fuzheng and Zheng, Zihan and Xiang, Yang and Yuan, Nicholas Jing and Xie, Xing and Li, Zhenhui},
    title={DRN: A Deep Reinforcement Learning Framework for News Recommendation},
    booktitle={Proceedings of the 2018 World Wide Web Conference},
    year={2018},
    month={April},
    pages={167--176},
    publisher={International World Wide Web Conferences Steering Committee},
    address={Geneva, Switzerland},
    series={WWW '18},
    doi={10.1145/3178876.3185994},
    url={https://doi.org/10.1145/3178876.3185994},
    isbn={978-1-4503-5639-8/18/04}
}

@article{liu2020secure,
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    volume={35},
    number={4},
    pages={70--82},
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    doi={10.1109/MIS.2020.2988525},
    url={https://ieeexplore.ieee.org/document/9142646},
    address={Piscataway, NJ, USA}
}

@article{kairouz2021advances,
    author={Kairouz, Peter and McMahan, H. Brendan and Avent, Brendan and Bellet, Aur{\'e}lien and Bennis, Mehdi and Bhagoji, Arjun Nitin and Bonawitz, Kallista and Charles, Zachary and Cormode, Graham and Cummings, Rachel and Guerraoui, Raouf and Harchaoui, Zaid and He, Chaoyang and He, Lie and Huo, Zhouyuan and Hutchinson, Ben and Ingerman, Alex and Jaggi, Martin and Javidi, Tara and Joshi, Gauri and Khodak, Mikhail and Kone{\v{c}}n{\'y}, Jakub and Korolova, Aleksandra and Koushanfar, Farinaz and Koyejo, Sanmi and Lepoint, Tancr{\`e}de and Liu, Yang and Mittal, Prateek and Mohri, Mehryar and Nock, Richard and {\"O}zg{\"u}r, Ayfer and Pagh, Rasmus and Raykova, Mariana and Qi, Hang and Ramage, Daniel and Raskar, Ramesh and Song, Dawn and Song, Weikang and Stich, Sebastian U. and Sun, Ziteng and Suresh, Ananda Theertha and Tram{\`e}r, Florian and Vepakomma, Praneeth and Wang, Jianyu and Xiong, Li and Xu, Zheng and Yang, Qiang and Yu, Felix X. and Yu, Han and Zhao, Sen},
    title={Advances and Open Problems in Federated Learning},
    journal={Foundations and Trends{\textregistered} in Machine Learning},
    year={2021},
    volume={14},
    number={1--2},
    pages={1--210},
    month={jun},
    publisher={Now Publishers, Inc.},
    address={Hanover, MA, USA},
    doi={10.1561/2200000083},
    url={https://doi.org/10.1561/2200000083},
    issn={1935-8237}
}

@inproceedings{mohri2019agnostic,
    author = {Mohri, Mehryar and Sivek, Gary and Suresh, Ananda Theertha},
    title = {Agnostic Federated Learning},
    booktitle = {Proceedings of the 36th International Conference on Machine Learning},
    year = {2019},
    editor = {Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
    volume = {97},
    series = {Proceedings of Machine Learning Research},
    pages = {4615--4625},
    month = {June},
    publisher = {PMLR},
    address = {Long Beach, California, USA},
    url = {https://proceedings.mlr.press/v97/mohri19a.html},
    doi = {10.48550/arXiv.1902.00146}
}

@inproceedings{ghosh2020efficient,
    author={Ghosh, Avishek and Chung, Jichan and Yin, Dong and Ramchandran, Kannan},
    title={An Efficient Framework for Clustered Federated Learning},
    booktitle={Advances in Neural Information Processing Systems},
    year={2020},
    volume={33},
    pages={19586--19597},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
    url={https://proceedings.neurips.cc/paper_files/paper/2020/file/145122ba645e000b9eb9654cf5d672cc-Paper.pdf},
    doi={10.48550/arXiv.2006.04088},
    month={December},
    note={NeurIPS 2020}
}

@inproceedings{zhang2020batchcrypt,
    author = {Zhang, Chengliang and Li, Suyi and Xia, Junzhe and Wang, Wei and Yan, Feng and Liu, Yang},
    title = {Batchcrypt: Efficient Homomorphic Encryption for Cross-Silo Federated Learning},
    booktitle = {Proceedings of the 2020 {USENIX} Annual Technical Conference ({USENIX} {ATC} 2020)},
    year = {2020},
    month = jul,
    publisher = {{USENIX} Association},
    address = {Online},
    pages = {493--506},
    url = {https://www.usenix.org/conference/atc20/presentation/zhang-chengliang},
    isbn = {978-1-939133-14-4}
}

@inproceedings{yurochkin2019bayesian,
    author={Yurochkin, Mikhail and Agarwal, Mayank and Ghosh, Soumya and Greenewald, Kristjan and Hoang, Nghia and Khazaeni, Yasaman},
    title={Bayesian Nonparametric Federated Learning of Neural Networks},
    booktitle={Proceedings of the 36th International Conference on Machine Learning},
    series={Proceedings of Machine Learning Research},
    editor={Chaudhuri, Kamalika and Salakhutdinov, Ruslan},
    volume={97},
    pages={7252--7261},
    year={2019},
    month={June},
    publisher={PMLR},
    address={Long Beach, California, USA},
    url={http://proceedings.mlr.press/v97/yurochkin19a.html},
    doi={10.48550/arXiv.1905.12022}
}

@inproceedings{liu2003building,
    author={Liu, Bing and Dai, Yang and Li, Xiaoli and Lee, Wee Sun and Yu, Philip S.},
    title={Building text classifiers using positive and unlabeled examples},
    booktitle={Proceedings of the Third IEEE International Conference on Data Mining},
    year={2003},
    pages={179--186},
    publisher={IEEE},
    organization={IEEE},
    address={Melbourne, FL, USA},
    month={November},
    doi={10.1109/ICDM.2003.1250919},
    url={https://doi.org/10.1109/ICDM.2003.1250919}
}

@article{liu2015classification,
    author={Liu, Tongliang and Tao, Dacheng},
    title={Classification with Noisy Labels by Importance Reweighting},
    journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
    year={2015},
    volume={38},
    number={3},
    pages={447--461},
    month={March},
    publisher={IEEE},
    address={Piscataway, NJ},
    issn={0162-8828},
    doi={10.1109/TPAMI.2015.2456899},
    url={https://ieeexplore.ieee.org/document/7115170}
}

@inproceedings{mcmahan2017communication,
    author={McMahan, Brendan and Moore, Eider and Ramage, Daniel and Hampson, Seth and {y Arcas}, Blaise Aguera},
    title={Communication-Efficient Learning of Deep Networks from Decentralized Data},
    booktitle={Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
    series={Proceedings of Machine Learning Research},
    volume={54},
    pages={1273--1282},
    year={2017},
    month={April},
    publisher={PMLR},
    address={Fort Lauderdale, FL, USA},
    url={http://proceedings.mlr.press/v54/mcmahan17a.html},
    doi={10.48550/arXiv.1602.05629}
}

@inproceedings{acar2021debiasing,
    title = {Debiasing model updates for improving personalized federated training},
    author = {Acar, Durmus Alp Emre and Zhao, Yue and Zhu, Ruizhao and Matas, Ramon and Mattina, Matthew and Whatmough, Paul and Saligrama, Venkatesh},
    booktitle = {International Conference on Machine Learning},
    pages = {21--31},
    year = {2021},
    editor = {Meila, Marina and Zhang, Tong},
    volume = {139},
    series = {Proceedings of Machine Learning Research},
    address = {Virtual},
    publisher = {PMLR},
    month = {jul},
    url = {https://proceedings.mlr.press/v139/acar21a.html},
    doi = {10.48550/arXiv.2102.04448}
}

@misc{geyer2017differentially,
    author={Geyer, Robin C and Klein, Tassilo and Nabi, Moin},
    title={Differentially private federated learning: A client level perspective},
    year={2017},
    month={dec},
    howpublished={arXiv preprint},
    eprint={1712.07557},
    archivePrefix={arXiv},
    url={https://arxiv.org/abs/1712.07557},
    primaryClass={cs.LG},
    keywords={Computer Science - Machine Learning, Computer Science - Cryptography and Security},
}

@inproceedings{lai2021oort,
    author={Lai, Fan and Zhu, Xiangfeng and Madhyastha, Harsha V. and Chowdhury, Mosharaf},
    title={Oort: Efficient Federated Learning via Guided Participant Selection},
    booktitle={Proceedings of the 15th USENIX Symposium on Operating Systems Design and Implementation},
    series={OSDI '21},
    year={2021},
    month={July},
    pages={19--35},
    publisher={USENIX Association},
    address={Berkeley, CA, USA},
    url={https://www.usenix.org/conference/osdi21/presentation/lai},
    note={15th USENIX Symposium on Operating Systems Design and Implementation (OSDI 21)},
    isbn={978-1-939133-22-9}
}

@inproceedings{wang2022enhancing,
    author={Wang, Lun and Xu, Yang and Xu, Hongli and Liu, Jianchun and Wang, Zhiyuan and Huang, Liusheng},
    title={Enhancing Federated Learning with In-Cloud Unlabeled Data},
    booktitle={2022 {IEEE} 38th International Conference on Data Engineering ({ICDE})},
    year={2022},
    pages={136--149},
    month={May},
    publisher={{IEEE}},
    address={Kuala Lumpur, Malaysia},
    doi={10.1109/ICDE53745.2022.00015},
    url={https://doi.org/10.1109/ICDE53745.2022.00015},
    isbn={978-1-6654-0885-7}
}

@inproceedings{reisizadeh2020fedpaq,
    author={Reisizadeh, Amirhossein and Mokhtari, Aryan and Hassani, Hamed and Jadbabaie, Ali and Pedarsani, Ramtin},
    title={FedPAQ: A Communication-Efficient Federated Learning Method with Periodic Averaging and Quantization},
    booktitle={Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics},
    series={Proceedings of Machine Learning Research},
    volume={108},
    pages={2021--2031},
    year={2020},
    publisher={PMLR},
    address={Palermo, Italy},
    month={Aug},
    url={https://proceedings.mlr.press/v108/reisizadeh20a.html},
    doi={10.18495/pmlr.v108.reisizadeh20a}
}

@article{wang2022feverless,
    author={Wang, Rui and Ersoy, O{\u{g}}uzhan and Zhu, Hangyu and Jin, Yaochu and Liang, Kaitai},
    title={Feverless: Fast and Secure Vertical Federated Learning Based on XGBoost for Decentralized Labels},
    journal={IEEE Transactions on Big Data},
    year={2022},
    volume={9},
    number={1},
    pages={295--308},
    month={September},
    publisher={IEEE},
    doi={10.1109/TBDATA.2022.3200877},
    url={https://ieeexplore.ieee.org/document/9883889},
    address={Piscataway, NJ, USA}
}

@article{xu2021efficient,
    author={Xu, Wuxing and Fan, Hao and Li, Kaixin and Yang, Kai},
    title={Efficient Batch Homomorphic Encryption for Vertically Federated XGBoost},
    journal={arXiv preprint arXiv:2112.04261},
    year={2021},
    month={December},
    eprint={2112.04261},
    eprinttype={arXiv},
    doi={10.48550/arXiv.2112.04261},
    url={https://arxiv.org/abs/2112.04261},
    publisher={arXiv},
    address={Ithaca, NY, USA},
    abstract={Federated learning (FL) has been proposed to enable collaborative machine learning training while preserving data privacy. However, most existing FL solutions either suffer from high communication overhead or rely on a trusted third party. This paper proposes an efficient batch homomorphic encryption scheme specifically designed for vertically federated XGBoost training, which achieves secure gradient computations without trusted third parties.}
}

@article{yao2022efficient,
    author = {Yao, Houpu and Wang, Jiazhou and Dai, Peng and Bo, Liefeng and Chen, Yanqing},
    title = {An Efficient and Robust System for Vertically Federated Random Forest},
    journal = {arXiv preprint},
    year = {2022},
    volume = {},
    number = {},
    pages = {},
    month = {January},
    eid = {arXiv:2201.10761},
    archivePrefix = {arXiv},
    eprint = {2201.10761},
    doi = {10.48550/arXiv.2201.10761},
    url = {https://arxiv.org/abs/2201.10761},
    publisher = {arXiv},
    address = {Cornell University},
    keywords = {Computer Science - Machine Learning, Statistics - Machine Learning}
}

@article{yang2019parallel,
    author = {Yang, Shengwen and Ren, Bing and Zhou, Xuhui and Liu, Liping},
    title = {Parallel Distributed Logistic Regression for Vertical Federated Learning Without Third-Party Coordinator},
    journal = {arXiv preprint arXiv:1911.09824},
    year = {2019},
    month = {November},
    url = {https://arxiv.org/abs/1911.09824},
    publisher = {arXiv},
    address = {Ithaca, NY},
    eprint = {1911.09824},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG}
}

@article{he2021secure,
    author={He, Daojing and Du, Runmeng and Zhu, Shanshan and Zhang, Min and Liang, Kaitai and Chan, Sammy},
    title={Secure logistic regression for vertical federated learning},
    journal={IEEE Internet Computing},
    year={2021},
    volume={26},
    number={2},
    pages={61--68},
    month={mar},
    publisher={IEEE},
    address={Los Alamitos, CA, USA},
    issn={1089-7801},
    doi={10.1109/MIC.2021.3059629},
    url={https://doi.org/10.1109/MIC.2021.3059629}
}

@inproceedings{lin2022federated,
    author={Lin, Xinyang and Chen, Hanting and Xu, Yixing and Xu, Chao and Gui, Xiaolin and Deng, Yiping and Wang, Yunhe},
    title={Federated Learning with Positive and Unlabeled Data},
    booktitle={International Conference on Machine Learning},
    series={Proceedings of Machine Learning Research},
    volume={162},
    pages={13344--13355},
    year={2022},
    month={July},
    publisher={PMLR},
    address={Baltimore, MD, USA},
    doi={10.48550/arXiv.2202.03392},
    url={https://proceedings.mlr.press/v162/lin22a.html}
}

@article{yang2019federated,
    author = {Yang, Qiang and Liu, Yang and Chen, Tianjian and Tong, Yongxin},
    title = {Federated Machine Learning: Concept and Applications},
    journal = {ACM Transactions on Intelligent Systems and Technology (TIST)},
    volume = {10},
    number = {2},
    pages = {1--19},
    year = {2019},
    month = {February},
    publisher = {ACM},
    address = {New York, NY, USA},
    issn = {2157-6904},
    doi = {10.1145/3298981},
    url = {https://doi.org/10.1145/3298981},
    issue_date = {January 2019}
}

@article{li2020federated,
    author = {Li, Tian and Sahu, Anit Kumar and Zaheer, Manzil and Sanjabi, Maziar and Talwalkar, Ameet and Smith, Virginia},
    title = {Federated Optimization in Heterogeneous Networks},
    journal = {Proceedings of Machine Learning and Systems},
    year = {2020},
    volume = {2},
    pages = {429--450},
    url = {https://proceedings.mlsys.org/paper/2020/file/73983c0198278f38d0c3eb87205d3ff5-Paper.pdf},
    doi = {10.48550/arXiv.1812.06127},
    publisher = {MLSys},
    address = {San Jose, CA, USA},
    month = {March},
    note = {Accessed: 2023-10-01}
}

@article{jeong2020federated,
    author={Jeong, Wonyong and Yoon, Jaehong and Yang, Eunho and Hwang, Sung Ju},
    title={Federated semi-supervised learning with inter-client consistency \& disjoint learning},
    journal={arXiv preprint arXiv:2006.12097},
    year={2020},
    month={jun},
    url={https://arxiv.org/abs/2006.12097},
    doi={10.48550/arXiv.2006.12097},
    publisher={arXiv},
    address={Ithaca, NY}
}

@article{acar2021federated,
    author = {Acar, Durmus Alp Emre and Zhao, Yue and Navarro, Ramon Matas and Mattina, Matthew and Whatmough, Paul N and Saligrama, Venkatesh},
    title = {Federated learning based on dynamic regularization},
    journal = {arXiv preprint},
    volume = {abs/2111.04263},
    year = {2021},
    month = {November},
    url = {https://arxiv.org/abs/2111.04263},
    eprint = {2111.04263},
    archiveprefix = {arXiv},
    primaryclass = {cs.LG},
    publisher = {arXiv},
    address = {Ithaca, NY, USA},
    keywords = {Computer Science - Machine Learning, Computer Science - Distributed, Parallel, and Cluster Computing}
}

@article{wang2020federated,
    author = {Wang, Hongyi and Yurochkin, Mikhail and Sun, Yuekai and Papailiopoulos, Dimitris and Khazaeni, Yasaman},
    title = {Federated Learning with Matched Averaging},
    journal = {arXiv preprint},
    volume = {arXiv:2002.06440},
    year = {2020},
    month = {February},
    url = {https://arxiv.org/abs/2002.06440},
    doi = {10.48550/arXiv.2002.06440},
    publisher = {arXiv},
    address = {Ithaca, NY, USA},
    keywords = {Computer Science - Machine Learning, Statistics - Machine Learning},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    eprint = {2002.06440},
    note = {Published in Proceedings of the 8th International Conference on Learning Representations (ICLR 2020)}
}

@article{konevcny2016federated,
    author={Kone{\v{c}}n{\`y}, Jakub and McMahan, H. Brendan and Yu, Felix X. and Richt{\'a}rik, Peter and Suresh, Ananda Theertha and Bacon, Dave},
    title={Federated Learning: Strategies for Improving Communication Efficiency},
    journal={arXiv preprint},
    volume={abs/1610.05492},
    year={2016},
    month={October},
    url={https://arxiv.org/abs/1610.05492},
    publisher={Cornell University},
    address={Ithaca, NY, USA},
    note={arXiv:1610.05492},
    archivePrefix={arXiv},
    eprint={1610.05492},
    primaryClass={cs.LG}
}

@article{diao2020heterofl,
    author = {Diao, Enmao and Ding, Jie and Tarokh, Vahid},
    title = {HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clients},
    journal = {arXiv preprint},
    volume = {arXiv:2010.01264},
    year = {2020},
    month = {October},
    url = {https://arxiv.org/abs/2010.01264},
    publisher = {arXiv},
    address = {Ithaca, NY, USA},
    note = {Published as a conference paper at ICLR 2021},
    archivePrefix = {arXiv},
    primaryClass = {cs.LG},
    eprint = {2010.01264},
    eprinttype = {arxiv}
}

@incollection{rivest1978data,
    author={Rivest, Ronald L. and Adleman, Len and Dertouzos, Michael L.},
    title={On Data Banks and Privacy Homomorphisms},
    booktitle={Foundations of Secure Computation},
    editor={DeMillo, Richard A. and Dobkin, David P. and Jones, Anita K. and Lipton, Richard J.},
    year={1978},
    publisher={Academic Press},
    address={New York, NY, USA},
    pages={169--180},
    note={Available from: \url{https://citeseerx.ist.psu.edu/document?repid=rep1\&type=pdf\&doi=2d0847e7cbfb756c4b09c53861fddc8d00b62984}},
    url={https://citeseerx.ist.psu.edu/document?repid=rep1&type=pdf&doi=2d0847e7cbfb756c4b09c53861fddc8d00b62984}
}

@article{sweeney2002k,
    author = {Sweeney, Latanya},
    title = {k-anonymity: {A} model for protecting privacy},
    journal = {International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems},
    year = {2002},
    volume = {10},
    number = {5},
    pages = {557--570},
    month = {October},
    publisher = {World Scientific},
    address = {Singapore},
    doi = {10.1142/S0218488502001648},
    url = {https://www.worldscientific.com/doi/abs/10.1142/S0218488502001648}
}

@inproceedings{dwork2008differential,
    author = {Dwork, Cynthia},
    title = {Differential Privacy: A Survey of Results},
    booktitle = {Theory and Applications of Models of Computation: 5th International Conference, TAMC 2008, Xi’an, China, April 25-29, 2008. Proceedings 5},
    year = {2008},
    editor = {Agrawal, Manindra and Du, Dingzhu and Duan, Zhenhua and Li, Angsheng},
    volume = {4978},
    series = {Lecture Notes in Computer Science},
    pages = {1--19},
    month = {April},
    publisher = {Springer},
    address = {Berlin, Heidelberg},
    doi = {10.1007/978-3-540-79228-4_1},
    url = {https://link.springer.com/chapter/10.1007/978-3-540-79228-4_1},
    organization = {Springer}
}

@inproceedings{zhao2018inprivate,
    author={Zhao, Lingchen and Ni, Lihao and Hu, Shengshan and Chen, Yaniiao and Zhou, Pan and Xiao, Fu and Wu, Libing},
    title={Inprivate digging: Enabling tree-based distributed data mining with differential privacy},
    booktitle={2018 IEEE Conference on Computer Communications (INFOCOM)},
    year={2018},
    pages={2087-2095},
    month={April},
    publisher={IEEE},
    address={Honolulu, HI, USA},
    doi={10.1109/INFOCOM.2018.8486351},
    url={https://ieeexplore.ieee.org/document/8486351},
    organization={IEEE}
}

@article{suykens1999least,
    author = {Suykens, Johan A. K. and Vandewalle, Joos},
    title = {Least Squares Support Vector Machine Classifiers},
    journal = {Neural Processing Letters},
    year = {1999},
    volume = {9},
    number = {3},
    pages = {293--300},
    month = {Nov},
    publisher = {Springer},
    address = {Dordrecht, Netherlands},
    issn = {1370-4621},
    eissn = {1573-773X},
    doi = {10.1023/A:1018628609742},
    url = {https://doi.org/10.1023/A:1018628609742}
}

@inproceedings{hanzely2020lower,
    author={Hanzely, Filip and Hanzely, Slavom{\'\i}r and Horv{\'a}th, Samuel and Richt{\'a}rik, Peter},
    title={Lower Bounds and Optimal Algorithms for Personalized Federated Learning},
    booktitle={Advances in Neural Information Processing Systems},
    volume={33},
    pages={2304--2315},
    year={2020},
    publisher={Curran Associates, Inc.},
    address={Red Hook, NY, USA},
    url={https://proceedings.neurips.cc/paper/2020/hash/6dd0880e67318b0e2df3a83b92f63a25-Abstract.html},
    doi={10.48550/arXiv.2006.08844},
    month={December},
    note={NeurIPS 2020}
}

@inproceedings{scott2009novelty,
    author={Scott, Clayton and Blanchard, Gilles},
    title={Novelty detection: Unlabeled data definitely help},
    booktitle={Proceedings of the Twelfth International Conference on Artificial Intelligence and Statistics},
    series={Proceedings of Machine Learning Research},
    editor={van Dyk, David and Welling, Max},
    volume={5},
    pages={464--471},
    year={2009},
    month={April},
    publisher={PMLR},
    address={Clearwater, Florida, USA},
    url={http://proceedings.mlr.press/v5/scott09a/scott09a.pdf},
    doi={10.5555/1816159.1816213},
    issn={1938-7228},
    isbn={978-1-929629-08-2}
}

@inproceedings{bonawitz2017practical,
    author={Bonawitz, Keith and Ivanov, Vladimir and Kreuter, Ben and Marcedone, Antonio and McMahan, H. Brendan and Patel, Sarvar and Ramage, Daniel and Segal, Aaron and Seth, Karn},
    title={Practical Secure Aggregation for Privacy-Preserving Machine Learning},
    booktitle={Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security},
    year={2017},
    month={oct},
    pages={1175--1191},
    publisher={Association for Computing Machinery},
    address={New York, NY, USA},
    doi={10.1145/3133956.3133982},
    url={https://doi.org/10.1145/3133956.3133982},
    isbn={978-1-4503-4946-8},
    series={CCS '17}
}

@inproceedings{li2020practical,
    author={Li, Qinbin and Wen, Zeyi and He, Bingsheng},
    title={Practical Federated Gradient Boosting Decision Trees},
    booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
    year={2020},
    volume={34},
    number={4},
    pages={4642--4649},
    month={February},
    publisher={AAAI Press},
    address={New York, NY, USA},
    doi={10.1609/aaai.v34i04.5893},
    url={https://ojs.aaai.org/index.php/AAAI/article/view/5893},
    issn={2374-3468},
    isbn={978-1-57735-835-0}
}

@article{pinkas2018scalable,
    author = {Pinkas, Benny and Schneider, Thomas and Zohner, Michael},
    title = {Scalable Private Set Intersection Based on {OT} Extension},
    journal = {ACM Transactions on Privacy and Security (TOPS)},
    year = {2018},
    volume = {21},
    number = {2},
    pages = {1--35},
    month = {Apr},
    doi = {10.1145/3152226},
    url = {https://doi.org/10.1145/3152226},
    publisher = {ACM},
    address = {New York, NY, USA},
    issn = {2471-2566},
    keywords = {Cryptographic protocols, Private set intersection, Oblivious transfer}
}

@inproceedings{pinkas2014faster,
    author={Pinkas, Benny and Schneider, Thomas and Zohner, Michael},
    title={Faster Private Set Intersection Based on {OT} Extension},
    booktitle={23rd {USENIX} Security Symposium ({USENIX} Security 14)},
    year={2014},
    month={August},
    pages={797--812},
    publisher={{USENIX} Association},
    address={San Diego, CA},
    isbn={978-1-931971-15-7},
    url={https://www.usenix.org/conference/usenixsecurity14/technical-sessions/presentation/pinkas},
    organization={{USENIX} Association}
}

@inproceedings{liang2004privacy,
    author={Liang, Gang and Chawathe, Sudarshan S.},
    title={Privacy-Preserving Inter-database Operations},
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    year      = {2021},
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@inproceedings{zhang2020tab,
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@inproceedings{brown2019differential,
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}